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Constraint-based learning reduces the burden of collecting labels by having users specify general properties of structured outputs, such as constraints imposed by physical laws.
Markov logic networks
Matthew Richardson and Pedro Domingos · 2006
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Guiding semi-supervision with constraint-driven learning
Ming-Wei Chang, Lev Ratinov, and Dan Roth · 2007
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Probabilistic graphical models: principles and techniques
Daphne Koller and Nir Friedman · 2009
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Smt-aided combinatorial materials discovery
Stefano Ermon, Ronan Le Bras, Carla P Gomes, Bart Selman, and R Bruce Van Dover · 2012
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Articulated human detection with flexible mixtures of parts
Yi Yang and Deva Ramanan · 2013
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Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
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Sequential max-margin event detectors
Dong Huang, Shitong Yao, Yi Wang, and Fernando De La Torre · 2014
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Semi-supervised learning with deep generative models
Diederik P Kingma, Shakir Mohamed, Danilo Jimenez Rezende, and Max Welling · 2014
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On-line learning of indoor temperature forecasting models towards energy efficiency
F Zamora-Martínez, P Romeu, P Botella-Rocamora, and J Pardo · 2014
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Tractable learning for structured probability spaces: A case study in learning preference distributions
Arthur Choi, Guy Van den Broeck, and Adnan Darwiche · 2015
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Pattern decomposition with complex combinatorial constraints: Application to materials discovery
Stefano Ermon, Ronan Le Bras, Santosh K Suram, John M Gregoire, Carla P Gomes, Bart Selman, and Robert Bruce van Dover · 2015
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Weakly-and semi-supervised learning of a dcnn for semantic image segmentation
George Papandreou, Liang-Chieh Chen, Kevin Murphy, and Alan L Yuille · 2015
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Constrained convolutional neural networks for weakly supervised segmentation
Deepak Pathak, Philipp Krahenbuhl, and Trevor Darrell · 2015
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Unsupervised representation learning with deep convolutional generative adversarial networks
Alec Radford, Luke Metz, and Soumith Chintala · 2015
Improved techniques for training gans
Tim Salimans, Ian Goodfellow, Wojciech Zaremba, Vicki Cheung, Alec Radford, and Xi Chen · 2016
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Convexification of learning from constraints
Iaroslav Shcherbatyi and Bjoern Andres · 2016
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Martin Arjovsky, Soumith Chintala, and Léon Bottou · 2017
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Improved training of wasserstein gans
Ishaan Gulrajani, Faruk Ahmed, Martin Arjovsky, Vincent Dumoulin, and Aaron Courville · 2017
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Infogail: Interpretable imitation learning from visual demonstrations
Yunzhu Li, Jiaming Song, and Stefano Ermon · 2017
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Max-margin deep generative models for (semi-) supervised learning
Chongxuan Li, Jun Zhu, and Bo Zhang · 2016
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Learning in implicit generative models
Shakir Mohamed and Balaji Lakshminarayanan · 2016
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Data programming: Creating large training sets, quickly
Alexander J Ratner, Christopher M De Sa, Sen Wu, Daniel Selsam, and Christopher Ré · 2016
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Takeru Miyato, Shin-ichi Maeda, Masanori Koyama, and Shin Ishii · 2017
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Label-free supervision of neural networks with physics and domain knowledge
Russell Stewart and Stefano Ermon · 2017
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A semantic loss function for deep learning with symbolic knowledge
Jingyi Xu, Zilu Zhang, Tal Friedman, Yitao Liang, and Guy Van den Broeck · 2017
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